The evolution of digital transformation: Adoption, execution and expansion in the wake of AI PDF Free Download
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Brightlayer report 2024
The evolution of
digital transformation
Adoption, execution and expansion in the wake of AI
Data collection and analysis by
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EATON Brightlayer Report 2024
Introduction
The evolving state of digital transformation
Sector deep dive: Manufacturing
Sector deep dive: Utilities
Sector deep dive: Buildings
Sector deep dive: Data centers
03
04
09
13
17
21
Source: 2024 S&P Global Market Intelligence 451 Research and Eaton custom survey
© 2024 Eaton All Rights Reserved. Eaton is a registered trademark. All other trademarks are property of their respective owners.
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EATON Brightlayer Report 2024
Introduction
Digital transformation has become an enterprise imperative like never
before. That said, in the two years since S&P Global Market Intelligence 451
Research and Eaton elded our rst survey and report, the Intersection of
Digital Transformation and the Energy Transition, global macroeconomic
trends, geopolitical challenges and massive ongoing technology change
have signicantly altered the landscape. Yet, some things remain the same:
Enterprises understand that digital transformation is not about adopting
new technologies but about fundamentally changing how businesses
operate and deliver value to customers.
Operational excellence and eciency are paramount, with automation
and real-time data analytics playing crucial roles in optimizing workows,
reducing costs and minimizing errors. Service and business innovation are
equally important, as the integration of IoT and edge computing enables the
creation of new products and services that enhance customer experiences
and support agile business models. Managing energy and power resources
to reach sustainability goals is another critical focus, with enterprises
adopting renewable energy sources and deploying energy management
systems to manage their carbon footprint and reduce operational costs.
In this 2024 report, the second in a series of surveys and analysis examining
the role of digital technologies for business excellence and transformation,
we look at what has changed in the past two years — and there’s been a
lot — and examine the best practices enterprises have adopted to achieve
their digital transformation and energy and power optimization goals
across four critical B2B sectors: manufacturing, utilities, building/facilities
management and data centers.
To inform this analysis, Eaton and S&P Global Market Intelligence 451
Research commissioned an international web survey conducted in March
and April 2024 of 1,381 respondents who are involved in their organizations'
digital transformation eorts in eight countries across North America,
Europe and the Middle East. See a complete description of the survey
details in the Methodology section at the end of this report.
Adoption of digital technologies and processes remained steady over the past two years, with some notable changes.
Organizations are nding it easier to overcome the inertia of relying on legacy technologies and processes, signaling a step forward in their
comfort level with digital change. And they’ve increased their digital skills via training and digital talent acquisition, laying the groundwork for
ongoing improvements.
Use of digital tools to manage energy and power remains a top—and slightly growing—priority. Reaching sustainability goals remains a
consistent digital driver, with eciency, cost savings and power optimization increasing as a primary energy management focus in the face of
macroeconomic challenges.
Cloud and cybersecurity remain the most-deployed digital technologies, with increased focus on AI emerging as the most signicant
new wildcard in the past two years. For industrial and energy-focused industries in particular, AI has the potential to make operations
more intelligent and automated. In ranked order, 29% of respondents have predictive AI or machine learning (ML) in use or plan today, 26%
generative AI, and 21% AI-enabled computer vision.
Sector takeaways:
• Manufacturers see promise in leveraging digital technologies — and increasingly AI — to optimize operations and improve
maintenance processes.
• Utilities are coming to depend on digital technology to address growing load capacity demand, viewing it as a signicant short-term aid to
long-term grid expansion and renewables adoption.
• Building operators have a strong focus on meeting sustainability targets, even as changes in demographics, customer demand and
working arrangements drastically change how they operate.
• Data center providers sit at the center of a data maelstrom, with rapid AI adoption driving demand for more powerful, plentiful compute
power — even outstripping their need to address energy and power requirements.
Finally, even as these seemingly disparate sectors face their own unique opportunities and challenges, the degree to which they are
interlinked and codependent is becoming more clear by the day. Soaring data center demand for power represents one of the utility sector’s
greatest challenges. Manufacturers of all sorts rely on ready access to power and compute resources, while the manufacture of increasingly
autonomous and electric vehicles impacts utilities and data centers alike. Building owners, meanwhile, must adjust to a changing world, turning
to digital insights as a bridge to the future.
Top takeaways
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EATON Brightlayer Report 2024
The evolving state of digital transformation
Although the domains of technology and digital transformation are evolving rapidly,
key enterprise adoption indicators — while high — have remained relatively at over
the past two years (see Figure 1). For instance, two years ago, 50% of respondents
considered themselves to be in the "execution" phase of digital transformation, and that
percentage is nearly the same in this year's study (47%). Similarly, the largest percentage
of respondents (74%) reported some adoption of digital tools two years ago, compared
to 78% today. One area that did show improvement is digital skills. In our previous
survey, 22% of respondents said their organization had "strong" digital skills, which
increased to 29% this year.
Key digital readiness indicators held steady – 2022 vs. 2024
Digital commitment
Execution: 50% 47%
Consideration: 47% 50%
No strategy: 3% 3%
Technology adoption
Broad: 22% 21%
Some: 74% 78%
Digital skills
Strong: 22% 29%
Growing: 74% 64%
Figure 1: Enterprises are committed to digital, but still largely in the learning phase
Q. Which of the following best describes the status of your organization's operational digital transformation strategy?
Q. Which of the following best describes the status of your organization's adoption of technology tools, technologies,
and solutions to support current or future operational digital transformation eorts?
Q. Which of the following best describes the capabilities and skills of the primary team implementing your
organization's current or future operational digital transformation eorts?
Base: All respondents – 2022 (n=1,001); 2024 (n=1,381). Sources: S&P Global Market Intelligence 451 Research and Eaton Digital Transformation Survey, 2022, 2024.
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EATON Brightlayer Report 2024
Q: Which of the following are drivers of your organization’s current or future digital transformation initiatives?
Base: All respondents – 2022 (n=1,001); 2024 (n=1,381).
Sources: S&P Global Market Intelligence 451 Research and Eaton Digital Transformation Survey, 2022, 2024.
Figure 2: Optimizing operations and reducing risk are the top drivers of digital adoption
Optimizing business
processes and operations
Reducing risk
(compliance, safety, data
protection, etc.)
Addressing energy
and power eciency/
transition goals
Improving supply chain/
trade network capabilities
Increasing overall
revenue/enhancing sales
Supporting ESG programs
Better capturing/
understanding customer
requirements
Saving money/cutting costs
Developing new or improving
existing products or services
(new sources of revenue)
Digital drivers
20222024
The question is whether greater improvement is likely. Overall, the picture looks bright. Just
3% of respondents said their organization has "no strategy" for digital transformation, and
only 2% reported "no adoption" of digital technologies. That suggests the market is still in the
learning stage, with strong intention to take advantage of digital tools and approaches. Those
improved digital skills should help drive adoption further, while the emergence of new, user-
friendly technologies — particularly generative AI — could encourage more companies to go
digital and do so more quickly.
Digital drivers also remained relatively unchanged, indicating consistency of visions and
goals. Optimizing processes and minimizing risk stayed steady as the top drivers. Addressing
energy usage was among the few drivers to see at least an upward tick (see Figure 2).
46%
42%
41%
40%
39%
34%
47%
44%
43%
41%
40%
38%
51%
25% 50% 75%
50%
49%
50%
46%
44%
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EATON Brightlayer Report 2024
While the primary forces driving digital transformation
forward remain relatively unchanged, at least two fairly
critical inhibitors improved substantially over the past two
years. Enterprises indicated they are less likely to face a
digital skills shortage, the second time survey responses
indicated improvements in this critical area. A more
digitally skilled sta means more rapid adoption with
fewer bumps in the road, multiplying its impact. At the
same time, respondents also indicated they are less likely
today to be dependent on — and reluctant to change —
legacy technology and business processes (see Figure 3).
Many rms, especially in the industrial sector, design
plants to be depreciated over the course of many
decades, so the addition of new instrumentation and
connectivity (i.e., IoT) to these browneld deployments
is an essential part of many digital transformations. By
comparison, swapping in new systems can cause negative
ripple eects, disrupting investment cycles, adding
retraining requirements and putting mission-critical
operations at risk. That fewer respondents view change —
whether it be updating browneld deployments or adding
new greeneld plant — as a roadblock represents a major
mind shift, and a potential instigator of digital change.
Among the industries surveyed, commercial building
and data center respondents were the least likely to cite
digital skill shortfalls or a desire to keep legacy processes
in place, making them more agile than their less nimble
manufacturing and utility peers.
Q: Which of the following are challenges to the adoption of digital transformation within your organization?
Base: All respondents – 2022 (n=1,001); 2024 (n=1,381). Sources: S&P Global Market Intelligence 451 Research and Eaton Digital Transformation Survey, 2022, 2024.
Figure 3: Enterprises are improving digital skills and removing digital roadblocks
Concerns about data privacy or security
Financial concerns (e.g., cost, budget,
return on investment)
Desire to maximize the value/life of existing assets
Regulatory or compliance concerns
Disconnect between IT and the business
Organizational complacency and/or conservative mindset
Lack of compelling or urgent use cases
Apprehension about business or operational risk
Lack of clear digital strategy or leadership
Over-reliance on legacy technology and processes
Shortage of sta/skilled resources
Digital inhibitors
20222024
40%
33%
33%
32%
30%
29%
26%
26%
24%
23%
23%
39%
35%
25% 50% 75%
34%
30%
30%
28%
26%
26%
24%
33%
29%
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EATON Brightlayer Report 2024
Cloud/As-a-Service
Cybersecurity
IoT sensors and platforms
Energy/power management platforms
Digital thread/digital twin
Edge computing
Connected/digitally-enabled legacy
hardware/machines/systems
Extended reality (XR)—metaverse, i.e.,
augmented reality (AR) / virtual reality (AR)
The digital toolbox
gets a new wrench: AI
The technologies and tools
organizations use to implement digital
transformation are evolving and
expanding quickly as well. In Figure
4, we compare the adoption changes
over our two surveys, focusing on
technologies that we asked about in
both years. Cloud and cybersecurity
deployment grew the most over the
past two years, cementing those two
critical technologies as linchpins of
digital transformation. Cloud changes
the economic and operational
underpinnings of information
technologies. Cybersecurity sits at the
crossroads of digital opportunity and
risk, ensuring that even as enterprises
open up digitally, they protect
themselves from theft and disruption.
Figure 4: Cloud and cybersecurity are foundational digital tools for transformation
20222024
Digital technology adoption
25% 50% 75%
Q: Which of the following technologies, tools or applications have you deployed or plan to deploy in the next 12 months to support your organization’s digital transformation?
Base: All respondents – 2022 (n=1,001); 2024 (n=1,381). Sources: S&P Global Market Intelligence 451 Research and Eaton Digital Transformation Survey, 2022, 2024.
79%
45%
38%
35%
20%
19%
10%
71%
73%
42%
33%
33%
17%
25% 50% 75%
17%
11%
80%
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EATON Brightlayer Report 2024
There are further jewels to be mined from our exploration of digital technologies. We added three technologies
in this year's survey, and one emerged as particularly signicant. To support operational transformation,
78% of respondents indicated they plan to deploy a private wireless network. Connectivity is critical to digital
transformation, and network options are proliferating, from campus Wi-Fi, to private cellular 5G and even so-
called xed wireless access oered by cellular operators to both home and, increasingly, business users. Both
autonomous machinery and blockchain remain in the yet-to-be or never-to-be adopted stage, with only 20% of
respondents saying they are using or plan to deploy them.
Finally, what about AI technologies such as ML, computer vision and — the popular new kid on the block —
generative AI? It is important to distinguish between these approaches that, while not the only AI modalities
available, are the ones most often used in operational environments. At the highest level is AI, the simulation of
human intelligence processes by machines. Machine learning systems improve performance over time without
being specically programmed. Computer vision enables systems to interpret and make decisions based on
images and videos. Generative AI systems can generate new content by learning underlying language and data
patterns. While there are other AI approaches — including deep or neural learning and expert systems —
machine learning, computer vision and GenAI are a good t for industry applications that aim to learn
complex processes, act in place of human sight, or create or consume content from machine instructions to
product manuals.
In our 2022 survey, AI was a single category, and 27% of respondents reported that they had deployed or were
planning to deploy AI. This year, we took a more ne-grained approach, including three AI-based categories: AI-ML
predictive, the use of AI models to learn from change and anticipate necessary actions; generative AI, the use of
large language models to generate content for conversational interfaces, enabling more natural interactions with
machines and services; and computer vision, an oshoot of the larger category of video analytics that relies on
machine sight to replace or augment worker oversight. The results (see Figure 5):
• AI/ML predictive is most in use, in deployment or plan, selected by 29% of respondents. However,
that percentage rises to 43% of respondents in the manufacturing sector, which has deployed machine
learning to automate assembly lines and improve maintenance programs.
• Generative AI is in deployment or plan by 26% of respondents, and it is deployed in relatively equal
measure across the four sectors surveyed. Generative AI is helpful in its own right, but it has the potential
to be an even bigger change agent when coupled with AI/ML, building agents that can act on their own with
intelligence and autonomy.
• Finally, computer vision is planned or deployed by 21% of respondents, again largely equally across
sectors. It is perhaps most pertinent in manufacturing, where computer vision can help in sorting or quality
control operations, but it has a place in other industries as well.
AI has great potential to contribute to digital transformation, bringing intelligence, automation and predictive
capabilities to critical industry use cases. We explore the expected impact of the many avors of AI as we dive
deep into the transformation imperatives of individual industries below.
Figure 5: Multiple forms of AI/ML will play a key
role in industrial transformation
AI current + planned adoption –
next 12 months
29%
26%
21%
Articial
intelligence:
Machine learning/
predictive
Articial
intelligence: Large
language model/
generative
Articial
intelligence:
Computer vision/
perceptual
Q. Which of the following technologies, tools or applications have
you deployed or plan to deploy in the next 12 months to support
your organization's operational digital transformation?
Base: All respondents – 2022 (n=1,001); 2024 (n=1,381). Sources: S&P Global Market
Intelligence 451 Research and Eaton Digital Transformation Survey, 2022, 2024.
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EATON Brightlayer Report 2024
The manufacturing sector is on a digital
journey, driven by increased production
volumes on one hand and better and more
voluminous instrumentation of factory
machinery and systems on the other. Those
twin trends provide manufacturers with
increasing amounts of digital data they
can use to gain greater understanding and
control of physical processes. Unlike the
IT industry, where technology refreshes of
equipment and software are frequent, a
manufacturing plant often has a life span of
many decades. While any new equipment
will likely be digitally instrumented and
connected, manufacturers must even more
frequently apply industrial IoT solutions to
existing plants (i.e., browneld deployments).
Indeed, today, digital budgets are more
targeted at browneld upgrades (cited by
41% of respondents) than installing entirely
new equipment (35%) (see Figure M1).
• Replacement of older equipment with more digitally-enabled installation as part of an
evolution toward OPEX services
• Brownelddigitalizationoflegacyequipment,aslackofCAPEXbudget means
replacement is not possible in short term
• OPEX focused on improving processes, rather than equipment, such as supply chain
integration or connected worker, through digital transformation
• WeseetominimizeOPEXand3rdpartydigitalservices,infavorofin-house operations
Deep dive: Manufacturing
Figure M1: Propensity of browneld vs. greeneld deployments
Q: When considering budgets directed toward ongoing operational improvement, what is your organization's primary decision driver?
(Base: Manufacturing respondents (n=345). Source: S&P Global Market Intelligence 451 Research and Eaton Digital Transformation Survey, 2024.
When considering budgets directed toward
ongoing operational improvement, what is
your organization's primary decision driver?
2.3%
34.8%
44.1%
18.8%
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EATON Brightlayer Report 2024
At the same time, through the evolution of industrial IoT and improved
plant connectivity, the focus is now also turning toward the industrial
workforce, with a goal of providing insights and support to facilitate and
enhance their role in the system. Greater deployment of AI and machine
learning is accelerating this trend, with manufacturers aiming to optimize
eciency via digital technologies while enhancing and augmenting —
rather than replacing — technical sta and teams. In fact, 19% of survey
respondents cited equipping their workers to be more connected as a
primary operational improvement driver.
Digital manufacturing priorities
Manufacturers typically fall into two broad categories: discrete (combining
components to make objects) or continuous process (processing raw
materials and combining them in an unbroken production). Depending on
what they are producing, manufacturers typically have a common set of
key performance indicators (KPIs), though relative priorities dier based
on the processes, opportunities and challenges of their operations. A
manufacturer’s highest-priority KPIs also strongly impact the direction of its
digital transformation investments and strategies.
For instance, all continuous process manufacturers indicated they believe
protability to be one of the most important KPIs to optimize, followed by
worker safety (91%) and uptime (89%). In comparison, just 55% of discrete
manufacturers cited uptime as a critical KPI while 82% reported that
tracking ESG targets is a priority (see Figure M2).
Figure M2: Digital manufacturing metrics to optimize dier by manufacturer type
Protability Worker
safety
Uptime Overall
equipment
eectiveness
(OEE)
Overall
operations
eectiveness
(OOE)
Emissions
intensity (i.e.,
optimization's
impact on
production
levels)
Total eective
equipment
performance
(TEEP)
ESG metrics
(environmental,
social,
governance)
On-time
delivery
Capacity
utilization
Discrete Continuous
Q: Of the KPIs your organization tracks, which ones are most important to work to optimize, i.e., have the most signicant impact on the business?Base:
Manufacturing respondents (n=345). Source: S&P Global Market Intelligence 451 Research and Eaton Digital Transformation Survey, 2024.
81% 82% 81% 80% 78% 78%
48%
63%
81%
55%
100%
90% 82%
78% 79%
76%
59% 57%
61%
88%
25%
50%
75%
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EATON Brightlayer Report 2024
Manufacturers cited some common concerns
about modernizing their maintenance
programs, regardless of the type of
manufacturer. Missing critical information is
the top challenge, indicating that there is still
a long way to go in digitizing plants. The next
most-cited barrier is organizational/workforce
resistance to technology. This is often forgotten
in a drive toward technology improvements —
people still matter in the process and must be
equipped with necessary tools and training, as
well as have a willingness to work with
the results.
While skepticism about predictive maintenance
is relatively high among manufacturers in
general, continuous processors trust predictive
maintenance oerings even less (with 48% citing
it as a challenge) than discrete manufacturers
(36%). Keeping a continuous process running
is about achieving an ongoing balance of
adjustments to remain in the “golden recipe”
window within long production runs, which in
turn drives routine maintenance approaches
and less expectation of predictive maintenance.
Figure M3: Discrete manufacturers more likely to be proactive
and predictive in their maintenance
Maintenance program digital maturity
Routine ProactiveResponsive Predictive
Discrete Continuous
Q: How would you best categorize the maturity of your maintenance program?
Source: S&P Global Market Intelligence 451 Research and Eaton Digital Transformation Survey, 2024.
(Base: Manufacturing Respondents, n=345)
Those numbers reect their respective operational and digital priorities. Continuous
manufacturers need a constant ow of product matching a recipe throughout; they
are unable to easily stop and start due to temperature, pressure and other attributes.
By comparison, a discrete process can absorb small breaks in processing for repairs
and upgrades and get back online quickly. Meanwhile, continuous processes are
often more energy-intensive than discrete manufacturing, and the cost of replacing
equipment and processes to address energy eciency accounts for the lower priority
placed on ESG and sustainability metrics.
People in the process: maintenance
Optimizing runtime operations is critical, so manufacturers also look to leverage data
insights to boost their maintenance programs. Traditional maintenance is based on
statistically calculated routines. Condition-based maintenance, based on insights
derived from machine and environmental data that impacts equipment condition,
can make maintenance a more responsive and proactive endeavor. Finally, by
leveraging advanced analytics and AI models, maintenance can become predictive;
manufacturers can anticipate problems even as a machine appears to be running as
usual, enabling even more informed prioritization of limited maintenance personnel.
Moving to more digitally driven maintenance processes is a journey, and as with plant
operations above, dierent types of manufacturers make that journey at dierent
paces. Overall, the largest percentage of manufacturers are in the responsive stage,
counting on data insights to make them less reactive but not fully putting their
maintenance programs in the hands of algorithms. That said, discrete manufacturers
are more aggressive than their continuous process peers in moving to more data-
driven maintenance approaches, with more opportunity to repair individual plant
components and the ability to start, stop or slow down production, which is more
disruptive in continuous process manufacturing (see Figure M3).
14%
41%
26%
17%
28%
42%
19%
11%
25%
50%
75%
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EATON Brightlayer Report 2024
AI in manufacturing
AI has been prevalent in manufacturing for many years with
predictive-style machine learning (ML) applied in plants to
help them operate more eciently. Increased amounts of
data due to ongoing plant digitization, however, makes it
even more challenging to interpret and derive context to
be of benet to increasingly digitally connected workforces.
The recent consumer interest in generative AI based on
large language models and prediction are beginning to make
their way into industrial applications. As personnel begin
to understand and apply these technologies to industrial
use cases — such as troubleshooting manuals, work orders
and support interactions — trust and reliance on AI as a
decision-making tool will begin to grow. Though many will
see AI as a silver bullet to solve all problems, some use
cases will benet from AI more than others. Perhaps most
notably, the digital use cases expected to be most impacted
by AI — such as digital twins, decarbonization and predictive
maintenance — are also among the least widely deployed
(see Figure M4). This indicates an expectation that AI can
solve those things that many have yet to implement, which
may be wishful thinking, though it is likely to become an
essential way to support engineers and mitigate workforce
and skills shortages.
Digital thread/digital twin to virtual track and
replicate physical operational processes
Facilitate decarbonization/sustainability metrics,
including ESG scoring
Using equipment data to predict asset failure/
performance degradation (predictive maintenance)
Enhancing maintenance, repair and operations (MRO)
with more preventive maintenance capabilities
Improving supply chain processes (i.e.,
encompassing logistics, warehousing, etc.)
Electrical energy IIoT monitoring and optimization
Update or replace legacy machinery to be more
digitally enabled
Equipping the workforce with digital tooling
Machine and plant IIoT monitoring and optimization
Industrial metaverse
AI will improve Deployed
Q: Digital use cases deployed today/expected impact of AI on use cases.
Base: Manufacturing respondents (n=345).
Source: S&P Global Market Intelligence 451 Research and Eaton Digital Transformation Survey, 2024.
Figure M4: Use cases like digital twin and predictive maintenance are expected to be improved with the addition of AI capabilities
Deployed use case and AI expectations
68%
34%
66%
30%
64%
60%
55%
45%
43%
42%
37%
50%
31%
48%
55%
21%
62%
38%
44%
43%
25% 50% 75%
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EATON Brightlayer Report 2024
Digital transformation is proving to be a critical enabler for
the utility sector, which must balance the transition to more
renewable energy resources against growing service demand.
While utilities will build out the grid and add new energy
resources over the course of the coming decades, data-driven
insights delivered today can squeeze more performance out
of the grid while helping to better bullet-proof operations.
Our survey reects those challenges, led by load growth.
Because the industry is so locally driven — organized and
regulated by region and facing unique service demands based
on location — its challenges are best viewed locally as well.
For example, utility load growth in the next 5-10 years, across
all regions, is expected to average 35%, led by the Middle East
at 37%, Western Europe and North America both at 36%, and
Central Europe and the Nordics at 27%. Sorting through the
numbers more closely, on average, 41% of utilities expect
load demand to grow up to 24%, while 29% expect growth
between 25% and 49%. In Western Europe, 29% of utilities
expect load demand to grow more than 50% whereas roughly
a third of utilities in the Middle East expect load growth above
50% (see Figure U1).
As our survey reects a point in time, expectations may rise as
the route to the all-electric society will likely lead to a surge in
demand driven by the electrication of transport and industry,
population growth and a changing climate.
Deep dive: Utilities
Figure U1: Expected load demand growth within the next 5-10 years by region
Q. By your best estimate, how do you anticipate your load demand changing in your service area in the next 5-10 years?
Base: Utilities respondents (n=346).
Source: S&P Global Market Intelligence 451 Research and Eaton Digital Transformation Survey, 2024.
+0-24%Decrease +25-49% +50-74% +75-100%
North America Western Europe Central Europe Middle East
25%
50%
75%
36%
39%
41%
60%
0%0%0%
29%
25%
34% 30%
10%
36%
23%
16%
6%
10% 7%
36%
0%0%
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EATON Brightlayer Report 2024
Not surprisingly, expected drivers of load growth also dier across regions. Utilities in urban
areas expect more growth from the electrication of transport than those in rural areas.
Not every utility has a power-intensive industry in its service area, and regional data center
growth and other macro trends can signicantly impact the outlook. In general, utilities
primarily expect growth of load demand to be generated by organic growth of residential,
commercial and industrial demand (cited by 38% of utilities), followed by electrication of
transport (32%) and electrication of industry (25%).
Grid challenges
In the energy transition, utilities face a range of challenges to keep the lights on while
accommodating growth, both operational and organizational. Respondents see the biggest
operational challenge as the transmission and distribution infrastructure being too light or
outdated to service increased electricity demand, according to 44% of utilities. Maintaining
grid stability while adding more distributed energy resources comes in second in Northern
America, according to 41% of respondents, while load shedding when there is too much
renewable energy takes second place in Western Europe (39%) and the Middle East (39%).
Those in Central Europe and Nordics indicated the availability of energy storage to be their
second biggest challenge (33%).
Figure U2: Top operational challenges to power continuity
Outdated
infrastructure
Grid stability
and renewables EV charging Load shedding Energy storage Grid resilience Undergrounding
North America Western Europe Eastern Europe Middle East
25%25%
50%50%
75%75%
41% 41% 35%
28% 32% 37% 31% 27% 22%
37% 39%
30%
38%
29% 32%
27% 19% 19% 15% 20% 18% 18% 19%27%26%
42% 42%
55%
Q: Which of the following are challenges to delivering power service to your customers?
Base: Utilities respondents (n=346).
Source: S&P Global Market Intelligence 451 Research and Eaton Digital Transformation Survey, 2024.
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EATON Brightlayer Report 2024
But utilities face more than just operational challenges. Given the
pace of the energy transition and rapidly changing technologies,
utilities also face signicant organizational challenges.
At the top of that list is changing business models — driven by
smart meters, more sophisticated customers and the move to
performance-based regulation — cited as the number one challenge
by 45% of utilities (see Figure U3). Other operational challenges are
more localized:
• In Northern America, changing and/or inconsistent regulations
and legislation (cited by 42% of respondents) is seen as an
additional inhibitor.
• In Central Europe and the Nordics, digitalization is a major
challenge, with 47% of utilities viewing it as new, unknown and/
or risky.
• In the Middle East, utilities indicate overly siloed organizations
to be their number one challenge (45%).
Q: Which of the following are challenges to delivering power service to your customers?
S&P Global Market Intelligence 451 Research and Eaton Digital Transformation Survey, 2024. Base: Utilities respondents (n=346).
Changing
business models Skills shortages
Overly-siloed Theft and revenue
leakage
Regulations
& legislation
Conservatism
& inertia
Digitization is new Insucient funding
25% 25%
50% 50%
75% 75%
48%
35%
38%
21%
41% 29%
35%
19%
42%
27%
36%
16%
26% 36%35%
22%
50%
34%35%
20%
33% 30%
48%
23%
41% 36%
46%
22%
29% 29%29% 29%
Figure U3: Organizational challenges to power continuity
North America Western Europe Eastern Europe Middle East
16
EATON Brightlayer Report 2024
Q: Utilities can add load capacity by building new plants or optimizing existing plants. How much increased
capacity do you expect to gain specically via the implementation of digital technologies?
Base: Utilities respondents (n=335). Source: S&P Global Market Intelligence 451 Research and Eaton Digital Transformation Survey, 2024.
Digital is key to change
Given the surging demand for power via organic growth and the electrication of transportation and industry, utilities must increase their power supply and expand
the capacity of the existing power grid. Adding renewable capacity, energy storage and grid upgrades have traditionally been the focus of discussions to address this
challenge, but the reality is that most physical grid expansion projects take years to complete, and adding distributed energy resources can endanger grid stability. For
these reasons, the discussion has turned to digitalization, which utilities see as critical to the expansion of grid capacity (see Figure U4).
On average, utilities expect digitalization can increase their load capacity on their existing T&D infrastructure by 26%. Digging deeper, 44% of utilities expect digitalization
of operations to result in up to 24% extra capacity on the existing grid; another 40% anticipate gains between 25% and 49%.
How does digitalization assist utilities in meeting their grid challenges? Digitally driven use cases that will help add capacity include capacity forecasting and management,
demand forecasting and response, outage detection and failure probability monitoring, predictive maintenance, demand planning, and vegetation analysis. In each of
these cases, data-driven insights can help utilities signicantly stretch current grid capacity, enabling them to better match supply and demand, anticipate and forestall
outages, and more proactively maintain critical equipment.
Figure U4: Extra grid capacity on current infrastructure through digitalization
+0-24%
We do not expect
to use digital
technologies at
all to enhance
our capacity
We will deploy
digital technology
but do not expect
it to increase load
capacity
+25-49%
+50-74%
North America Western Europe Central Europe Middle East
25%
50%
75%
11%
44% 45%
41%
2% 1%
2% 3% 3%10%
17%
37%
8%
40% 40%
48% 48%
0% 0% 0%
17
EATON Brightlayer Report 2024
In 2024, building owners and operators are at the conuence of major societal
factors, from the emergence of exible hybrid work environments to the
requirement to facilitate energy eciency upgrades. To ourish in this rapidly
changing environment, facility managers are taking stock of their building
inventory, including downsizing in favor of more connected, energy-ecient
buildings. Commercial building tenants, meanwhile, are focused on managing
hybrid work arrangements and providing an enhanced employee experience
to retain top talent and ease return-to-oce mandates.
C&I building digital opportunities and challenges
As in 2022, most building owners in this year's survey cited sustainability as
their primary smart building driver, a function of regulatory mandates, societal
priority and the long-term operational cost savings associated with going green
(see Figure B1).
Deep dive: Commercial and institutional buildings
Figure B1: Sustainability ranks highest among smart building drivers
Q: When considering your organization's plans to upgrade building function/capabilities using digital
technologies, which of the following drivers would you consider to be the primary motivation?
Base: Building respondents (n=345).
Source: S&P Global Market Intelligence 451 Research and Eaton Digital Transformation Survey, 2024.
To be sustainable – become 'greener', comply with
targets/regulations, contributing to societal goals
To be remarkable – stand out from other buildings,
create compelling inhabitant experience
To be resilient – function in the face of natural and
man-made disasters and operational challenges
2022 2024
46%
28%
26%
49%
28%
24%
25% 50% 75%
18
EATON Brightlayer Report 2024
While sustainability ranked above the desire to be resilient or
remarkable for the group as a whole, digital drivers varied by building
size. For instance, large building respondents were signicantly more
likely (64%) than their medium (43%) and small building (26%) peers to
cite sustainability as their chief motivator for smart building initiatives.
Smaller building respondents were more likely to focus on being
remarkable (47%) as their primary motivator in their eort to create a
compelling, personalized occupant experience for building users.
Cost remains the top barrier to smart building deployments, with 52%
of respondents citing return on investment or cost benet analysis
as a hindrance to deployments. Small building owners, meanwhile,
are more likely than their peers to be challenged by the complexity
of digital technology or the need to meet regulatory mandates, likely
because small building owners tend to be more resource-constrained.
C&I building use cases: outcomes and
technologies
In pursuit of sustainability improvements, building owners focus on
digital-enabled initiatives such as improving energy eciency, reducing
environmental impact and increasing their use of renewables. A range
of technologies can help them achieve those goals (see Figure B2).
While small building owners may lag their larger peers in terms of
technology adoption to achieve green goals, it’s not for lack of interest
in going green. Small buildings exceed their larger counterparts in
deployments in two of three main sustainability outcomes: addressing
sustainability targets and improving overall energy eciency.
Medium and large building owners, meanwhile, are more likely
to deploy new digital technologies to aid them in reaching their
sustainability goals. These include deploying building management
systems, installing EV charging infrastructure and pursuing a transition
to renewables.
Figure B2: Small building respondents diverge from medium and large peers on green technologies
Buildings: green outcomes
Buildings: green technologies
Q: Which of the following energy- and power-specic smart building use cases have you deployed or plan to deploy in the next 12 months within your organization?
Base: Building respondents (n=345).
Source: S&P Global Market Intelligence 451 Research and Eaton Digital Transformation Survey, 2024.
Address sustainability targets
(i.e., net-zero carbon, etc.)
Deploy electrical power management
system (EPMS) platform and dashboard
In-building/campus-wide microgrids
Deploy building management system
(BMS) platform and dashboard
Deploy EV charging systems
Transition to renewable energy sources
Improve overall energy eciency
Large Medium Small
43%
45%
41%
42%
38%
29%
54%
46%
41%
40%
39%
36%
39%
43%
46%
46%
33%
44%
23%
42%
30%
25% 50%
25% 50% 75%
75%
19
EATON Brightlayer Report 2024
Size isn’t the only factor that impacts technology adoption.
We found variances in smart building use-case adoption based
on geography. For example, respondents in North America
and Western Europe cited environmentally focused smart
building deployments more than other regions, while Middle
East respondents are deploying video surveillance more than
those in other geographies (see Figure B3). Central Europe
and Nordic respondents lagged their peers in the application
of all but two use cases: space utilization/people ow and the
deployment of a building digital twin. Digital twins, though
nascent, oer promise in optimizing building management
and energy eciency from planning to operations through the
integration of real-time data across multiple physical systems
into a single interactive platform that replicates physical
operations. The Central Europe leadership in digital twins
could be attributed to leapfrogging legacy technologies in favor
of newer approaches, or because of top-down or government-
led initiatives to modernize infrastructure.
AI in buildings
Articial intelligence is also playing a growing role in the
building sector. In a smart building optimized by and for AI,
one may expect demand-responsive heating and cooling,
energy supply optimization, and highly personalized in-building
user experiences (Figure B4). Q: Which of the following digitally enabled use cases have you deployed or plan to
deploy in the next 12 months to improve building operations?
Base: Building respondents (n=345). Source: S&P Global Market Intelligence 451 Research and Eaton Digital
Transformation Survey, 2024.
Figure B3: Geographies diverge on traditional smart building technology adoption
Environmental monitoring
Predictive maintenance of
building systems
Space utilization/people ow
Video surveillance/safety
Inhabitant comfort monitoring
Building digital twin
North America Western Europe Eastern Europe Middle East
78%
73%
31%
31%
26%
21%
66%
52%
20%
22%
32%
30%
71%
69%
40%
30%
27%
25%
62%
80%
50%
37%
32%
12%
25% 50% 75%
20
EATON Brightlayer Report 2024
The building use cases with the highest anticipated AI impact
include people-centric applications, such as space utilization/
people ow (66% of respondents citing a high level of AI
impact), inhabitant comfort monitoring (58% AI impact) and
sensor-enabled access control/people ow (58% AI impact).
Yet those use cases are also among the least-deployed today,
according to our survey. This points to both the promise of
AI-driven smart building applications and the relatively longer
road to adoption.
Given both the opportunities and risks of placing AI at the
center of tomorrow’s smart buildings, owners and operators
must carefully consider their digital drivers and goals and
then work with technology vendors to examine how AI may
help them reach those outcomes. Although there is signicant
hype surrounding AI’s capabilities in less commonly deployed
use cases, implementations reveal more subtle impacts
due to real-world complexities, integration challenges or
overestimated expectations. Setting achievable goals — such
as reducing energy cost through optimization in the rst year
— and being transparent about potential hurdles can align
expectations with reality and lead to more sustainable long-
term implementations.
Figure B4: AI impact and smart building deployments
Q: For the use cases you selected, which ones do you trust to be inuenced or enhanced by articial
intelligence (AI) technologies?
Base: Building respondents (n=345).
Source: S&P Global Market Intelligence 451 Research and Eaton Digital Transformation Survey, 2024.
Space utilization/people ow
Environmental monitoring
Sensor-enabled access control/intrusion detection
Energy eciency management (overall energy usage,
power consumption, etc.)
Inhabitant comfort monitoring
Video surveillance/safety
Deployed AI impact
29%
72%
31%
71%
26%
56%
66%
50%
59%
48%
58%
41%
25% 50% 75%
21
EATON Brightlayer Report 2024
Data center operators today — whether commercial providers or enterprises
running their own data centers — sit at a tipping point of great change. Just
two years ago, terms such as "ChatGPT" and "GenAI" were mentioned mainly
in data scientist circles; today, they resonate with nearly every end user, driving
massive new data volumes from the edge to the cloud. That data tsunami comes
on top of a decade of IoT growth, particularly at industrial rms, where equally
large amounts of machine and sensor data have been set free from operational
environments to be stored and analyzed in data centers small and large.
These demand and capacity challenges arrive at the same time that data
center sustainability is under heavy scrutiny. Data centers are the great energy
consumers of the 21st century, replacing factories and cities, with an almost
endless thirst for electricity and a dire need to optimize power, cooling and heat
utilization. With government, regulators and businesses increasingly focused on
sustainability, data center operators are embracing greener technologies and
practices, including integrating renewable energy sources, optimizing cooling
systems and implementing advanced energy management solutions. And they
must do all of this without compromising on service reliability and security or
incurring prohibitive costs.
Our survey results reect that range of challenges, with service-oriented needs
(such as ensuring security and delivering new capabilities) at the top followed by
an array of capacity- and demand-related challenges (see Figure D1). Notably, just
35% of all data center respondents cited meeting sustainability goals in this year's
survey, a slight drop from 37% two years ago. Even more surprising, however, is
that the same percentage (35%) of data center respondents in Western Europe,
where carbon targets are more pressing, cited sustainability as a challenge facing
their operations.
Deep dive: Data centers
Figure D1: Data centers face service, capacity and sustainability challenges
Q: Which of the following are challenges your organization faces in operating its data centers?
Base: Data center respondents (n=345). Source: S&P Global Market Intelligence 451 Research and Eaton Digital Transformation Survey, 2024.
Security (e.g., cyberattacks, data theft)
Replacing aging infrastructure
Meeting rising performance requirements
Meeting sustainability goals and metrics
Delivering new services and capabilities (e.g.,
edge performance, cloud native, etc.)
Optimizing energy/power consumption
Meeting growing infrastructure demands
Monitoring equipment performance/
utilization/resiliency
Understanding usage and deploying
capacity for AI
56%
Service
challenges
Capacity
challenges
Up next AI
Sustainability
challenges
38%
42%
35%
45%
35%
40%
32%
28%
25% 50% 75%
22
EATON Brightlayer Report 2024
Sustainability concerns less
prominent
That a concern for sustainability challenges
lags both service and capacity challenges
in 2024 becomes even clearer as we look
at sustainability-specic data center goals.
Two years ago, data centers placed a
greater relative emphasis on sustainability
projects and goals. Today, challenged by
the service capacity demands of AI, IoT
and edge computing, that green focus
has receded, with a smaller percentage of
data respondents prioritizing data center
sustainability goals across the board
compared to two years ago (see Figure D2).
Figure D2: Compared to just two years ago, and in the face of growing demand, data center sustainability goals are less prominent
Increase the use of renewable energy sources
Improve energy storage capabilities
(hydrogen fuel cell, lithium-ion
Promote energy eciency/sustainability 'story' to
regulators, markets and customers
Provide energy/power data to more people,
technicians to C-suite
Lower overall energy/power costs
Achieve power usage eectiveness (PUE) targets
Turn energy/power management/storage from cost
to revenue center
Reduce waste (e.g., materials, heat, etc.)
Operate an overall carbon neutral facility
Leverage energy credits/carbon osets
Reduce/eliminate use of diesel generators
Reduce water usage/consumption
Deploy micro-grids
41%
37%
36%
31%
30%
28%
28%
26%
23%
22%
21%
17%
16%
50%
47%
40%
41%
40%
36%
34%
37%
28%
28%
20%
22%
16%
Q: Thinking specically about the eciency and sustainability of your data center, which considerations or goals guide your organization’s eorts today?
Base: Data center respondents (n=345). Sources: S&P Global Market Intelligence 451 Research and Eaton Digital Transformation Survey, 2022, 2024.
2024 2022 25% 50% 75%
23
EATON Brightlayer Report 2024
Of the dierent types of data center operators
we surveyed — including hyperscalers, regional/
colocation providers and local/edge operators
— only the cloud hyperscalers emphasized
sustainability to the same degree they did two
years ago, placing increased use of renewables,
reduced use of diesel generators and
decarbonization among their project priorities.
The obvious question is how to explain these
results, given that sustainability remains a high
point of focus in the sector. One explanation
is that addressing environmental and
decarbonization issues is now commonplace
and well understood, making it less of a
"challenge" for data center operators, especially
when they are also faced with exploding,
AI-driven demand. While a requirement,
addressing sustainability issues is no longer a
problem to be solved but, rather, a strategy to
be executed.
At the same time, it is critical to understand
exactly where the attention has shifted. The
answer is primarily to data center projects
focused on addressing demand and capacity
challenges. The top four data center projects
prioritized over the next 12 months are pitched
in that direction, emphasizing facility upgrades,
capacity expansion and improvement utilization
(see Figure D3). Dierent types of operators
emphasize dierent points, but the focus on
amping up capacity remains the one constant. Q: Which of the following data center projects, if any, is a priority for your organization over the next 12 months?
Base: Data center respondents (n=345). Source: S&P Global Market Intelligence 451 Research and Eaton Digital Transformation Survey, 2024.
Figure D3: Data center operators are scaling up to meet growing demand
Top data center projects
(all respondents)
Project priorities
by data center type
Vs.
Close data centers or consolidate
excess capacity
Commercial data center
• Upgrade existing facility
• Build new data center
Hyperscale
• Improve utilization
• Build new data centers
Enterprise data centers
• Expand capacity
• Upgrade existing facility
Expand overall data center capacity
Upgrade/retrot an existing facility
Improve IT asset performance/
utilization (e.g., servers, storage)
32.5%
32.8%
42.3%
38.6%
10% 30%20% 40%
24
EATON Brightlayer Report 2024
Data center use cases and AI
Data center operators are not only inundated with customer data, but data about their own operations as well.
By incorporating sensors and instrumentation systems — from network to compute to cooling and more — data
centers gain the ability to use data to optimize their own facilities, improve service levels and reduce costs. Today,
the top digital use cases in deployment by data center operators are led by cyberattack/data theft prevention,
predictive maintenance of data center equipment, and data center surveillance and control.
Notably, data center providers — typically tech-savvy operations in their own right — look forward to the impact that
AI can have on their operations. According to our survey, they foresee AI not only making their network, system and
power monitoring more predictive, but also becoming elemental in enabling data center digital twins, providing a
real-time visualization of their facilities that can become the living, breathing hub of their operations center.
Summary
As we saw in our rst digital transformation survey, industries such as manufacturing, utilities, building/facilities
services and data centers are both emboldened and challenged by digitalization. The pace of adoption remains
steady, although the most signicant drivers and opportunities have evolved. Optimizing operations and cutting
costs remains a major focus, with energy and power management a critical component in these sectors. At the
same time, it’s critical to note that many companies are still near the starting line, with major advancements such as
generative AI just now coming to the fore. Being open to digital change while building long-term digital capabilities
and scoring near-term wins is the best approach as the enterprise digital transformation journey continues.
Methodology
This report is based on a commissioned web survey conducted in March/April 2024. The respondents were qualied
based on their responsibilities in their organization’s operational digital transformation and their inuence on the
purchase of technology solutions enabling it. Respondent companies were from diverse industries and company
sizes of 100+ full-time employees. Total sample size for the study is 1,381 (US, n=300; Canada, n=120; Mexico,
n=120; UK, n=120; France, n=120; Germany, n=120; The Netherlands, n=120; Italy, n=120; Denmark, n=44; Finland,
n=42; Switzerland, n=34; UAE, n=66; Saudi Arabia, n=55).
Roles of the respondents t into one of four eligible industry sectors: building/facilities services; data center owner/
provider (including colocation and edge); manufacturing/industrial; and utilities. Survey respondents were at
the director level and above in IT, operational technology, facilities management and energy/sustainability roles.
Respondents were screened to be purchase decision-makers for embedded operations technology, having some
sort of responsibility or connection in their role to operations technology for the site/facility. Their connection to
operations technology could be either for IT or other mechanical ops. The survey was executed blindly — i.e., the
survey sponsor name was not revealed to the participants at any stage of the project.
About S&P Global Market Intelligence 451 Research
451 Research provides essential insight into key trends driving digital transformation across the entire technology
landscape. By oering a combination of expert analyst insight and dierentiated data, 451 Research enables the
industry with the information and perspectives they require to make more eective decisions.
Our research is organized into channels that align with the prevailing key issues driving digital transformation. These
channels are: Applied Infrastructure & DevOps; Cloud & Managed Services Transformation; Cloud Native; Customer
Experience & Commerce; Data, AI & Analytics; Data center Services & Infrastructure; ESG; Fintech; Information
Security; Internet of Things; and Workforce Productivity & Collaboration. For more information about 451 Research,
please go to: spglobal.com/451research.
About S&P Global Market Intelligence
At S&P Global Market Intelligence, we understand the importance of accurate, deep and insightful information. Our
team of experts delivers unrivaled insights and leading data and technology solutions, partnering with customers
to expand their perspective, operate with condence, and make decisions with conviction. S&P Global Market
Intelligence is a division of S&P Global (NYSE: SPGI). S&P Global is the world’s foremost provider of credit ratings,
benchmarks, analytics and workow solutions in the global capital, commodity and automotive markets. With every
one of our oerings, we help many of the world’s leading organizations navigate the economic landscape so they can
plan for tomorrow, today. For more information, visit www.spglobal.com/market intelligence.
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